Signal Processing and Machine Learning, 30 cr
Type of the study module
Sari Peltonen, Heikki Huttunen, Joni Kämäräinen
|-||Student is able to study state-of-the-art in machine learning and signal processing methods and can adopt and adapt these techniques.
Student is able to design, train and deploy a machine learning model for classification and regression. She can also design a system for processing data from the domains of one of the three sub-modules.
|Study block||Credit points||Mandatory/Advisable||Additional information|
|30 cr||Mandatory||If the student finishes a B.Sc. degree elsewhere than Computing and Electrical Engineering at TUT, then the prerequisite courses are determined by those of the individual courses of this study module.|
In addition to the two compulsory courses (10 credits), the student must select one of the three Selective Sub-modules: 1) Imaging and Computer Vision 2) Artificial Intelligence, or 3) Audio and Signal Processing (required courses for each are listed below).
|SGN-21006 Advanced Signal Processing||5 cr||IV|
|SGN-41007 Pattern Recognition and Machine Learning||5 cr||IV|
Optional Compulsory Courses
Optional compulsory courses are divided under the three selective sub-modules (20cr each): 1) Imaging and Computer Vision, 2) Artificial Intelligence and 3) Audio and Signal Processing. Student must select one of the sub-modules, but we also support other creative combinations (in that case contact the professors responsible for the module).
Must be selected at least 20 credits of courses
|ASE-7536 Model-Based Estimation||5-7 cr||2||IV|
|IHA-4406 Fundamentals of Robot Vision||5 cr||1||IV|
|MAT-02456 Fourier Methods||4 cr||3||IV|
|MAT-60350 Tilastolliset monimuuttujamenetelmät||5 cr||3||IV|
|MAT-61006 Introduction to Functional Analysis||7 cr||1||V|
|MAT-62006 Inverse Problems||7 cr||1||V|
|MAT-62256 Advanced Functional Analysis||7 cr||1||V|
|SGN-22006 Signal Compression||5 cr||3||IV|
|SGN-24007 Advanced Audio Processing||5 cr||3||IV|
|SGN-25006 Vector Space Methods for Signal and Image Processing||5 cr||1, 3||IV|
|SGN-26006 Advanced Signal Processing Laboratory||5 cr||1, 2, 3||IV|
|SGN-31007 Advanced Image Processing||5 cr||1||IV|
|SGN-33007 Media Analysis||5 cr||1, 2, 3||IV|
|SGN-34006 3D and Virtual Reality||5 cr||1||IV|
|SGN-43006 Knowledge Mining and Big Data||5 cr||2, 3||IV|
|SGN-44006 Artificial Intelligence||5 cr||2||IV|
|SGN-45006 Fundamentals of Robot Vision||5 cr||2||IV|
|SGN-81006 Signal Processing Innovation Project||5-8 cr||1, 2, 3||IV|
|TIE-22307 Data-Intensive Programming||5 cr||2||IV|
Select 20 credits.
Selective Module 1: Imaging and Computer Vision.
Only one of the functional analysis courses MAT-61006 and MAT-62256 can be included. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
2. Select 20 credits. Selective Module 2: Artificial Intelligence and Machine Learning. Course SGN-44006 Artificial Intelligence is compulsory for students reading this selective module. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
3. Select 20 credits. Selective Module 3: Audio and Signal Processing. Course SGN-24007 Advanced Audio Processing is compulsory for students reading this selective module. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
Additionally, the student may propose alternative courses to substitute items from the above list.
This MSc (Eng.) major module strengthens students' knowledge on modern signal processing and machine learning. Module will specifically focus on state-of-the-art technologies and approaches that are requested in the top notch companies of data engineering, autonomous cars, robotics and so on. Under this major module there are three optional sub-modules which provide in-depth and state-of-the-art knowledge in one of the three most emerging fields: 1) vision and imaging, 2) audio and signal processing and 3) artificial intelligence. The major will thus consist of 10 cr of common courses and at least 20 cr of courses in one of the three sub-modules. This way we provide strong knowledge on signal processing and machine learning and one additional special area where the skills become expert level. All three modules provide strong knowledge and skills appreciated by the companies and especially their R&D units.